Document Type : Research Paper


1 Computer and Information Department, College of Electronics Engineering, Ninevah University, Mosul, Iraq

2 Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq

3 Artificial Intelligence, Software, and Information Systems Engineering Departments, AI and Robotics Institute, Near East University, Nicosia, Mersin10, Turkey


Environmental monitoring and industrial automation use WSNs extensively. Since sensor nodes have limited batteries,
WSNs must be energy efficient. LEACH helps WSNs capture energy-efficient data. Cluster heads affect LEACH protocol
energy consumption and network lifespan. This paper improves LEACH protocol cluster head selection with a Genetic
Algorithm. The program chooses cluster heads that maximize network energy efficiency. Cluster heads represent
solutions in the Genetic Algorithm's genetic model. Energy efficiency measures fitness. Selection, crossover, and
mutation boost fitness. We simulated extensively to test our strategy. We compared LEACH-GA, the original LEACH
protocol, and various optimization methods. This article shows 100% network lifespan improvement compared to various
routing protocols, including LEACH-C, FIGWO, GA-LEACH, PSO, ABC-SD, CGTABC2& ACO, LEACH, I-LEACH,
54% compared to ED-LEACH, and 28% compared to GADA-LEACH. The LEACH-GA algorithm outperforms the
baseline LEACH algorithm and other algorithms in energy efficiency, network lifetime, and data aggregation. Our paper
introduces a novel cluster head selection strategy for the LEACH protocol, which advances WSNs. Genetic Algorithms
do this. The LEACH-GA algorithm increases energy efficiency and network longevity. Thus, it offers a feasible solution
for energy-constrained WSN applications to help build and deploy effective WSN protocols, improving sensor network
sustainability and dependability.


Main Subjects